Results on precision and F1 score. We further reported precision and F1 score to give a more comprehensive evaluation.
According to the results on CIFAR100-LT-IB-100 and ImageNet-LT, the low precision and high recall are observed on (1) many-shot subset for the imbalanced model (XE); (2) few-shot set for the balanced models.
This indicates that the imbalanced models are “head” biased and the balanced models are “tail” biased.
In contrast, on ImageNet-LT, our xERM\(_{\!P\!C}\)  achieves both high recall and precision (recall: \(67.6\%\) , precision: \(53.1\%\) ) on the many-shot subset compared to factual model XE that achieves high recall with lower precision (recall: \(68.6\%\) , precision: \(46.9\%\) ), and achieves high recall and precision on the few-shot subset (recall: \(24.0\%\) , precision: \(52.4\%\) ) compared to PC [1]} that achieves competitive recall with lower precision (recall: \(23.8\%\) , precision: \(32.0\%\) ).
These results demonstrate the unbiasedness of our proposed method.
Besides, an unbiased model should have a balanced precision over the three subsets. For example, in Table REF , our xERM\(_{\!P\!C}\)  manifests \(53.1\%\) , \(55.0\%\)  and \(52.4\%\)  over many-shot, medium-shot and few-shot precision with a small variance, while XE manifests \(46.9\%\) , \(59.1\%\)  and \(60.7\%\)  and PC manifests \(56.3\%\) , \(49.7\%\)  and \(32.0\%\)  with large variances.
As a result, our xERM achieves the highest F1 score on all the three subsets.
